基于改进相似性度量的扩展置信规则库规则激活方法

摘要

针对扩展置信规则库(extended belief rule base,EBRB)系统在计算个体匹配度时会出现负值以及所有规则的激活权重都为零的问题,引入基于属性权重的欧氏距离,对传统的EBRB中相似性度量方法进行改进.此外,传统的规则激活方法激活的是所有激活权重大于零的规则,并没有考虑到规则之间的不一致性,而规则的不一致性会减弱EBRB系统的推理能力,因此将激活规则之间的不一致性也考虑进来,提出一种基于改进相似性度量的规则激活方法.与传统的扩展置信规则激活方法相比,新的规则激活方法通过设置阈值来激活规则,这些被激活的规则不仅是激活权重大于零的规则,而且是不一致性最小的规则集合.最后,利用输油管道泄漏问题和多个公共分类数据集对新的规则激活方法的有效性进行验证,实验结果表明,基于改进的相似性度量的规则激活方法能够有效提高EBRB系统的推理准确性.%When calculating negative individual matching degrees,there might appear negative values and all rules' activation weights may be equal to zero.To address this problem,this paper introduces the Euclidean distance which is based on attribute weights and improves the traditional similarity computational formula.In addition,the traditional rule activation method activates all rules whose activation weights are greater than zero without considering inconsistency which exists in the activated rules,since the inconsistency of activated rules will weaken the reasoning performance of EBRB systems.Hence,considering the inconsistency existing in the activated rules,a new rule activation method of EBRB based on improved similarity measures is proposed.Compared with traditional rule activation method in the EBRB,the proposed approach activates rules by setting thresholds.And these activated rules are not only greater than zero but also have the smallest inconsistency.Finally,the pipeline leak detection problem and multiple public classification datasets have been employed to validate the efficiency of the new rule activation method.The experimental results show that the proposed method based on improved similarity measures can improve the reasoning accuracy of EBRB systems.